A feature matching-based method for few-shot multivariate time series anomaly detection with symmetric patch mask Siam Transformer

Accurate anomaly detection of industrial system operating status based on multivariate time series data is an important means to ensure the stable operation of the system. However if there is insufficient training data for the objects to be detected, it is difficult for existing deep learning methods to learn a clear outline of the normal pattern of the data under unsupervised conditions, leading to the failure of anomaly detection. This paper proposes a feature matching-based method for few-shot multivariate time series anomaly detection with a symmetric patch mask Siam Transformer (SPMST). Using only a small number of normal samples from the target domain, SPMST realizes the rapid deployment of the universal representation model pre-trained on multiple public datasets to the target domain without the need for retraining or parameter adjustment for more categories. First, two augmented views of the original data are obtained by adding a symmetric patch mask to the augmented aligned multisource data. The Transformer model is then pre-trained with reconstruction and contrastive learning tasks to acquire robust latent representations. Second, the feature support set of the target domain is obtained based on the pre-trained representation model and the proposed clustering-based support set reduction strategy, avoiding excessive consumption of computing resources. Finally, the anomaly score is calculated by combining the feature matching loss, reconstruction loss, and contrastive loss. The experimental results show that SPMST, under few-shot conditions, is not weaker than 21 state-of-the-art baselines trained with a large amount of data on 5 representative cyber–physical system datasets.